Using Remote Sensing Spectral Image Dynamics for early prediction of biotic stress in wheat: lessons from Armenia and southern Russia

Winter and spring wheat are among key agricultural crops in the Republic of Armenia, and represent a significant share of grain production. However, their yield is threatened to substantially decline due to the negative impact of various biotic factors, including weeds and phytopathogens such as rust, powdery mildew, and tan spot. Remote sensing methods, particularly multitemporal dynamics of plant spectral imagery, offer opportunities for early detection and monitoring of these diseases. Early identification allows for timely management interventions to stabilize crop conditions, preserve yields, and enable mapping of problem areas before scheduled applications, allowing more effectively application of herbicides and fungicides.

Hyperspectral spectrometry of winter wheat crops under increased pathogen stress, together with control plots without increased pathogen stress, were studied in experimental fields in southern Russia (Krasnodar Krai) between 2017-2023. The results show that the temporal dynamics in reflectance during the spring-summer growth period of winter wheat likely indicate disease levels, where the period between stem elongation and heading was identified as crucial. A series of high-frequency spectral measurements (every 2–3 days) allowed the classification of areas with infected and healthy plants (accuracy of 70–88%) but also reasonably accurate predictions of the maximum development stage of various pathogens (R² = 0.48–0.55) 10–12 days before peak development. Moreover, these patterns were confirmed using data from ground-based spectrometry, UAVs, and satellite imagery.

Additionally, this methodology was tested on spring wheat fields in the Republic of Armenia (Aragatsotn, Nerkin Sasnashen) in 2024. Using a series of multitemporal UAV surveys, the fields were divided into zones based on the temporal behavior of spectral imagery that successfully identifies zones of weed emergence and negative consequences of agronomic errors. However, identification of more sensitive spectral regions with pathogen hotspots was hindered by the high heterogeneity of the fields.

Based on these methodologies, we defined the optimal dates for initiating phytosanitary monitoring for different regions in Armenia. This part of the investigation shows that zoning territories by the timing of the phenophase "stem elongation" with an error <10 days is crucial for the start of intensive spectral monitoring, and can be achieved by combining NDVI data with meteorological and topographical parameters.

Altogether, the results demonstrate the early diagnosis of biotic stress in plants is feasible using spectral data and can improve decision-making for field treatments in the long term. The early detection of biotic stress in plants enhances the potential of precision agriculture, as time is a crucial factor in addressing these challenges. Furthermore, the described methods have shown the capability to be scaled from local experiments, as is currently the case in most studies, to a regional scale.